Serrano-Candelas, E. | bioRxiv Science

bioRxiv2024

MITF Targets in Gastrointestinal Stromal Tumors: Implication in Autophagy and Extracellular Vesicle Secretion

Previous studies have identified Microphthalmia-associated Transcription Factor (MITF) involvement in regulating Gastrointestinal Stromal Tumors (GIST) growth and cell cycle progression. This study uses Chromatin Immunoprecipitation combined with high-throughput sequencing (ChIP-seq) and RNA sequencing to explore MITF-modulated genes in GIST. Our findings reveal that MITF regulates genes involved in lysosome biogenesis, vesicle generation, autophagy, and mTOR signaling pathways. Comparative transcriptome analysis following MITF silencing in GIST cells shows differential enrichment in mTOR signaling, impacting tumor growth and autophagy. In the context of cancer, the interplay between autophagy and extracellular vesicle release can influence tumor progression and metastasis. We examined MITFs role in autophagy and extracellular vesicle (EV) production in GIST, finding that MITF overexpression increases autophagy, as shown by elevated LC3II levels while silencing MITF disrupts autophagosome and autolysosome formation. Despite no significant changes in EV size or number, MITF silencing notably reduces KIT expression in EV content. KIT secretion in EVs has been linked to GIST metastasis, suggesting that MITF is a crucial target for managing tumor growth and metastasis in GIST.

cancer biology↗

bioRxiv2024

Systematic Evaluation of High-Throughput PBK Modelling Strategies for the Prediction of Intravenous and Oral Pharmacokinetics in Humans

Physiologically based kinetic (PBK) modelling offers a mechanistic basis for predicting the pharmaco-/toxicokinetics of compounds and thereby provides critical information for integrating toxicity and exposure data to replace animal testing with in vitro or in silico methods. However, traditional PBK modelling depends on animal and human data, which limits its usefulness for Non-Animal Methods. To address this limitation, High-throughput PBK modelling aims to rely exclusively on in vitro and in silico data for model generation. Here, we evaluate a variety of in silico tools and different strategies to parameterise PBK models with input values from various sources in a high-throughput manner. We gather 2000+ publicly available human in vivo concentration-time profiles of 200+ compounds (IV and oral administration), as well as in silico, in vitro and in vivo determined compound-specific parameters required for the PBK modelling of these compounds. Then, we systematically evaluate all possible PBK model parametrisation strategies in PK-Sim and quantify their prediction accuracy against the collected in vivo concentration-time profiles. Our results show that even simple, generic High-throughput PBK modelling can provide accurate predictions of the pharmacokinetics of most compounds (87% of Cmax and 84% of AUC within 10-fold). Nevertheless, we also observe major differences in prediction accuracies between the different parameterisation strategies, as well as between different compounds. Finally, we outline a strategy for High-throughput PBK modelling that relies exclusively on freely available tools. Our findings contribute to a more robust understanding of the reliability of High-throughput PBK modelling, which is essential to establish the confidence necessary for its utilisation in Next-Generation Risk Assessment.

pharmacology and toxicology↗